Efforts to make radiotherapy treatment better, faster, and more cost effective have been underway since radiation was first used to treat cancer. The current radiation treatment workflow is mostly human based. First is a diagnostics/staging step where patients go through imaging, biopsy, CT/PET simulation and the staging of the cancer. A tumor definition and prescription is then outlined by a Radiation Oncologist, who draws the tumor contour based on imaging techniques such as CT/PET/MRI and gives the “prescription” dosages to a dosimetrist. The dosimetrist, or other such expert, then outlines the treatment planning which includes 1) drawing the contours of region of interests such as heart, cord, esophagus etc., (this can also be done by auto segmentation and graphical tools); 2) designing the beam directions and angles based on trial and error; 3) optimizing the beam intensities using objection function parameters based on trial and error; and 4) checking the plan with the radiation oncologist to determine if the plan is acceptable. If the plan is not accepted by the radiation oncologist, the radiation oncologist will ask the dosimetrist to modify the plan. The dosimetrist will then repeat the previous steps. Once the Radiation oncologist accepts the plan, the dosimetrist compiles all of the plan information and sends the plan to a delivery database and/or clinical station. Physicists check, quality assure and approve the plan. The therapy may be divided into fractions, (one per day, 5 days per week, for example). A radiation therapist uses the final plan to deliver the treatment to the patient.
Although IMRT treatment planning methods have improved continuously over the years, IMRT treatment planning is still a complex process that depends strongly on the medical dosimetrist's experience (Schwarz 2009). For instance, the dosimetrist specifies beam directions based on past experience and trial-and-error, and then specifies objectives for dose distribution using single dose values, a few dose—volume points, or fully flexible dose—volume histograms (DVHs). Objectives may be weighted based on their importance. The planning system represents these objectives in a cost function, which must be maximized or minimized using an optimization algorithm. The cost function numerically attempts to represent the tradeoffs that are incorporated into clinical judgment. If the dosimetrist wants to change the outcome, he or she can iteratively alter the objectives and re-optimize. However, it is difficult to translate clinical requirements into a cost function and ‘steer’ the optimization toward the best result. As a result, IMRT planning can be a time-consuming and frustrating task, and the quality of treatment plans with similar target dose prescriptions and normal tissue constraints will vary between treatment dosimetrists and institutions (Schwarz 2009).
It is believed that the plan quality improvement is significant to improve the overall radiation therapy healthcare quality. Although IMRT can provide better outcomes for some cancers, the clinical benefits of this treatment can be compromised by sub-optimal treatment planning. In 2003, Forster, Smythe et al. 2003 reported that IMRT could provide a local control rate of greater than 80%, with acute toxicity below grade 3, in pleural mesothelioma, a largely fatal disease with an aggressive clinical course and a high mortality rate. This technology was immediately adopted by Mass General Hospital (MGH). Allen et al. (Allen, Czerminska et al. 2006) subsequently reported fatal (grade>=4) thoracic radiation penumonities (TRPs) in 6 of 13 patients receiving IMRT treatment. After much debate, (Komaki, Liao et al. 2006; Allen and Baldini 2007; Allen, Schofield et al. 2007; Rodrigues and Roa 2007; Veldeman, Madani et al. 2008) it was concluded that the high TRPs seen by Allen et al. may have been due to less strict treatment planning objectives. Importantly, Veldeman et al. (Veldeman, Madani et al. 2008) used a similar technique to that of MDACC (MD Anderson Cancer Center) and did not observe fatal TRPs. Veldeman et al. concluded that “we operate at the verge of what is clinically tolerable. Such an aggressive regimen should therefore only be delivered within strictly defined protocols, with rigorous quality control and potential candidates selected with extreme caution.” From the above description, it can be speculated that the quality of IMRT planning varies from institution to institution, and only the best designed IMRT plans offer therapeutic advantages. It can also be speculated that if the AutoPlan system would be available to MGH at the time when they adopted the mesothelioma treatment technology, it would be possible that fatal radiation damage to patients could have been avoided.
With the IMRT technique available to more and more community setting hospitals, it is very hard to ensure the plan quality. There is a long learning curve for IMRT planners, demonstrated in quality comparisons between new and seasoned dosimetrists. This learning curve was confirmed (Chung, Lee et al. 2008) by a recent plan quality comparison study for same plans designed by National University Hospital, Singapore and University of California-San Francisco. After this study, Chung et. al. concluded that “our IMRT plans were not able to fully maximize the potential dosimetric gains of IMRT over 3DCRT”. Even for the big institution like MDACC, the learning cure for a new technology is also not very short. For example, the first case of mesothelioma case treated in MDACC took 8 weeks from simulation to treatment. After four years experiences on the planning constrains and beam angle selections, the treatment planning time was reduced to 1 week. It can be imagined that it is almost impossible to let community hospital to perform those complex treatments if they started from scratch. The AutoPlan system will be a vehicle to rapidly spread the newest treatment technologies to more radiation therapy facilities.
Embodiments of the invention presented here, including a method, system and computer readable medium designs a treatment plan in order to improve the quality and consistency of treatment planning. This method 1) automatically sets beam angles based on a beam angle automation (BAA) algorithm that is expert system based, and/or 2) automatically adjusts the objectives of the objective function based on an objective function parameter automation (OFPA) algorithm. The treatment plan provides methods for delivering a prescribed radiation dose to a predefined target volume while attempting to avoid giving large dose to tissue and organs surrounding the target volume.
Embodiments of the present invention relate to a novel method to select beam-angles and objective-function parameters in order to create an optimized treatment plan. A goal is to set beam-angle and objective/cost function parameters. The algorithm is executed in a reasonable time frame so that it can be used in routine clinical practice. Other methods, systems, features and advantages of the present invention will be or become apparent to one with skill in the art upon examination of the following drawings and detailed description.